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Dedupe Rank

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dedupe-rank

Dedupe Rank is an Agent Skill for documents published in the WILLOSCAR/research-units-pipeline-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Use when a broad paper candidate pool needs deterministic deduplication and a stable core set. **Trigger**: dedupe, rank, core set, 去重, 排序, 精选论文, 核心集合. **Use when**: 检索后需要把广覆盖集合收敛成可管理的 core set（用于 taxonomy/outline/mapping）。 **Skip if**: 已经有人手工整理了稳定的 `papers/core_set.csv`（无需再次 churn）。 **Network**: none. **Guardrail**: 偏 deterministic；输出应可重复（稳定 paper_id、字段规范）。

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/WILLOSCAR/research-units-pipeline-skills/blob/HEAD/.codex/skills/dedupe-rank/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/dedupe-rank, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Dedupe + Rank

Turns a raw candidate pool into a deduped pool and a stable core set.

Input

  • papers/papers_raw.jsonl

Outputs

  • papers/papers_dedup.jsonl
  • papers/core_set.csv

Script boundary

scripts/run.py should own only:

  • title/year deduplication
  • deterministic ranking
  • stable paper_id generation

Use shared domain packs or pipeline contract metadata for topic-specific or product-specific behavior.

Contract-driven behavior

The script should prefer pipeline contract metadata over profile-name branching.

Current important field:

  • quality_contract.candidate_pool_policy.keep_full_deduped_pool

If true, the script keeps the full deduped pool in papers/core_set.csv unless the user explicitly overrides core size.

Acceptance

  • deduped JSONL exists
  • core-set CSV exists
  • reruns are stable for the same inputs

Non-goals

  • retrieval
  • screening
  • manual topic authoring inside the script